Measurement of low‐density lipoprotein cholesterol levels in primary and secondary prevention patients: Insights from the PALM registry

Background The 2013 American College of Cardiology/American Heart Association Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults recommended testing low-density lipoprotein cholesterol ( LDL -C) to identify untreated patients with LDL -C ≥190 mg/dL, assess lipid-lowering therapy adherence, and consider nonstatin therapy. We sought to determine whether clinician lipid testing practices were consistent with these guidelines. Methods and Results The PALM (Patient and Provider Assessment of Lipid Management) registry enrolled primary and secondary prevention patients from 140 US cardiology, endocrinology, and primary care offices in 2015 and captured demographic data, lipid treatment history, and the highest LDL -C level in the past 2 years. Core laboratory lipid levels were drawn at enrollment. Among 7627 patients, 2787 (36.5%) had no LDL -C levels measured in the 2 years before enrollment. Patients without chart-documented LDL -C levels were more often women, nonwhite, uninsured, and non-college graduates (all P\u3c0.01). Patients without prior lipid testing were less likely to receive statin treatment (72.6% versus 76.0%; P=0.0034), a high-intensity statin (21.5% versus 24.3%; P=0.016), nonstatin lipid-lowering therapy (24.8% versus 27.3%; P=0.037), and had higher core laboratory LDL -C levels at enrollment (median 97 versus 92 mg/dL; P\u3c0.0001) than patients with prior LDL -C testing. Of 166 individuals with core laboratory LDL -C levels ≥190 mg/dL, 36.1% had no LDL -C measurement in the prior 2 years, and 57.2% were not on a statin at the time of enrollment. Conclusions In routine clinical practice, LDL -C testing is associated with higher-intensity lipid-lowering treatment and lower achieved LDL -C level

Systematic review and network meta-analysis on the efficacy of evolocumab and other therapies for the management of lipid levels in hyperlipidemia

Background: The proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors evolocumab and alirocumab substantially reduce low‐density lipoprotein cholesterol (LDL‐C) when added to statin therapy in patients who need additional LDL‐C reduction. Methods and Results: We conducted a systematic review and network meta‐analysis of randomized trials of lipid‐lowering therapies from database inception through August 2016 (45 058 records retrieved). We found 69 trials of lipid‐lowering therapies that enrolled patients requiring further LDL‐C reduction while on maximally tolerated medium‐ or high‐intensity statin, of which 15 could be relevant for inclusion in LDL‐C reduction networks with evolocumab, alirocumab, ezetimibe, and placebo as treatment arms. PCSK9 inhibitors significantly reduced LDL‐C by 54% to 74% versus placebo and 26% to 46% versus ezetimibe. There were significant treatment differences for evolocumab 140 mg every 2 weeks at the mean of weeks 10 and 12 versus placebo (−74.1%; 95% credible interval −79.81% to −68.58%), alirocumab 75 mg (−20.03%; 95% credible interval −27.32% to −12.96%), and alirocumab 150 mg (−13.63%; 95% credible interval −22.43% to −5.33%) at ≥12 weeks. Treatment differences were similar in direction and magnitude for PCSK9 inhibitor monthly dosing. Adverse events were similar between PCSK9 inhibitors and control. Rates of adverse events were similar between PCSK9 inhibitors versus placebo or ezetimibe. Conclusions: PCSK9 inhibitors added to medium‐ to high‐intensity statin therapy significantly reduce LDL‐C in patients requiring further LDL‐C reduction. The network meta‐analysis showed a significant treatment difference in LDL‐C reduction for evolocumab versus alirocumab

Lipid levels are inversely associated with infectious and all-cause mortality: international MONDO study results.

Cardiovascular (CV) events are increased 36-fold in patients with end-stage renal disease. However, randomized controlled trials to lower LDL cholesterol (LDL-C) and serum total cholesterol (TC) have not shown significant mortality improvements. An inverse association of TC and LDL-C with all-cause and CV mortality has been observed in patients on chronic dialysis. Lipoproteins also may protect against infectious diseases. We used data from 37,250 patients in the international Monitoring Dialysis Outcomes (MONDO) database to evaluate the association between lipids and infection-related or CV mortality. The study began on the first day of lipid measurement and continued for up to 4 years. We applied Cox proportional models with time-varying covariates to study associations of LDL-C, HDL cholesterol (HDL-C), and triglycerides (TGs) with all-cause, CV, infectious, and other causes of death. Overall, 6,147 patients died (19.2% from CV, 13.2% from infection, and 67.6% from other causes). After multivariable adjustment, higher LDL-C, HDL-C, and TGs were independently associated with lower all-cause death risk. Neither LDL-C nor TGs were associated with CV death, and HDL-C was associated with lower CV risk. Higher LDL-C and HDL-C were associated with a lower risk of death from infection or other non-CV causes. LDL-C was associated with reduced all-cause and infectious, but not CV mortality, which resulted in the inverse association with all-cause mortality

Ezetimibe therapy: mechanism of action and clinical update.

The lowering of low-density lipoprotein cholesterol (LDL-C) is the primary target of therapy in the primary and secondary prevention of cardiovascular events. Although statin therapy is the mainstay for LDL-C lowering, a significant percentage of patients prescribed these agents either do not achieve targets with statin therapy alone or have partial or complete intolerance to them. For such patients, the use of adjuvant therapy capable of providing incremental LDL-C reduction is advised. One such agent is ezetimibe, a cholesterol absorption inhibitor that targets uptake at the jejunal enterocyte brush border. Its primary target of action is the cholesterol transport protein Nieman Pick C1 like 1 protein. Ezetimibe is an effective LDL-C lowering agent and is safe and well tolerated. In response to significant controversy surrounding the use and therapeutic effectiveness of this drug, we provide an update on the biochemical mechanism of action for ezetimibe, its safety and efficacy, as well as the results of recent randomized studies that support its use in a variety of clinical scenarios

Developing a modified low-density lipoprotein (M-LDL-C) Friedewald’s equation as a substitute for direct LDL-C measure in a Ghanaian population: a comparative study

Despite the availability of several homogenous LDL-C assays, calculated Friedewald\u27s LDL-C equation remains the widely used formula in clinical practice. Several novel formulas developed in different populations have been reported to outperform the Friedewald formula. This study validated the existing LDL-C formulas and derived a modified LDL-C formula specific to a Ghanaian population. In this comparative study, we recruited 1518 participants, derived a new modified Friedewald\u27s LDL-C (M-LDL-C) equation, evaluated LDL-C by Friedewald\u27s formula (F-LDL-C), Martin\u27s formula (N-LDL-C), Anandaraja\u27s formula (A-LDL-C), and compared them to direct measurement of LDL-C (D-LDL-C). The mean D-LDL-C (2.47±0.71 mmol/L) was significantly lower compared to F-LDL-C (2.76±1.05 mmol/L), N-LDL-C (2.74±1.04 mmol/L), A-LDL-C (2.99±1.02 mmol/L), and M-LDL-C (2.97±1.08 mmol/L) p \u3c 0.001. There was a significantly positive correlation between D-LDL-C and A-LDL-C (r=0.658, p\u3c0.0001), N-LDL-C (r=0.693, p\u3c0.0001), and M-LDL-C (r=0.693, p\u3c0.0001). M-LDL-c yielded a better diagnostic performance [(area under the curve (AUC)=0.81; sensitivity (SE) (60%) and specificity (SP) (88%)] followed by N-LDL-C [(AUC=0.81; SE (63%) and SP (85%)], F-LDL-C [(AUC=0.80; SE (63%) and SP (84%)], and A-LDL-C (AUC=0.77; SE (68%) and SP (78%)] using D-LDL-C as gold standard. Bland-Altman plots showed a definite agreement between means and differences of D-LDL-C and the calculated formulas with 95% of values lying within ±0.50 SD limits. The modified LDL-C (M-LDL-C) formula derived by this study yielded a better diagnostic accuracy compared to A-LDL-C and F-LDL-C equations and thus could serve as a substitute for D-LDL-C and F-LDL-C equations in the Ghanaian population

Interleukin-6 blockade raises LDL via reduced catabolism rather than via increased synthesis: a cytokine-specific mechanism for cholesterol changes in rheumatoid arthritis

Objectives Patients with rheumatoid arthritis (RA) have reduced serum low-density lipoprotein cholesterol (LDL-c), which increases following therapeutic IL-6 blockade. We aimed to define the metabolic pathways underlying these lipid changes. Methods In the KALIBRA study, lipoprotein kinetic studies were performed on 11 patients with severe active RA at baseline and following three intravenous infusions of the IL-6R blocker tocilizumab. The primary outcome measure was the fractional catabolic rate (FCR) of LDL. Results Serum total cholesterol (4.8 vs 5.7 mmol/L, p=0.003), LDL-c (2.9 vs 3.4 mmol/L, p=0.014) and high-density lipoprotein cholesterol (1.23 vs 1.52 mmol/L, p=0.006) increased following tocilizumab therapy. The LDL FCR fell from a state of hypercatabolism to a value approximating that of the normal population (0.53 vs 0.27 pools/day, p=0.006). Changes in FCR correlated tightly with changes in serum LDL-c and C-reactive protein but not Clinical Disease Activity Index. Conclusions Patients with RA have low serum LDL-c due to hypercatabolism of LDL particles. IL-6 blockade normalises this catabolism in a manner associating with the acute phase response (and thus hepatic IL-6 signalling) but not with RA disease activity as measured clinically. We demonstrate that IL-6 is one of the key drivers of inflammation-driven dyslipidaemia

Lack of effect of lowering LDL cholesterol on cancer: meta-analysis of individual data from 175,000 people in 27 randomised trials of statin therapy

<p>Background: Statin therapy reduces the risk of occlusive vascular events, but uncertainty remains about potential effects on cancer. We sought to provide a detailed assessment of any effects on cancer of lowering LDL cholesterol (LDL-C) with a statin using individual patient records from 175,000 patients in 27 large-scale statin trials.</p> <p>Methods and Findings: Individual records of 134,537 participants in 22 randomised trials of statin versus control (median duration 4.8 years) and 39,612 participants in 5 trials of more intensive versus less intensive statin therapy (median duration 5.1 years) were obtained. Reducing LDL-C with a statin for about 5 years had no effect on newly diagnosed cancer or on death from such cancers in either the trials of statin versus control (cancer incidence: 3755 [1.4% per year [py]] versus 3738 [1.4% py], RR 1.00 [95% CI 0.96-1.05]; cancer mortality: 1365 [0.5% py] versus 1358 [0.5% py], RR 1.00 [95% CI 0.93–1.08]) or in the trials of more versus less statin (cancer incidence: 1466 [1.6% py] vs 1472 [1.6% py], RR 1.00 [95% CI 0.93–1.07]; cancer mortality: 447 [0.5% py] versus 481 [0.5% py], RR 0.93 [95% CI 0.82–1.06]). Moreover, there was no evidence of any effect of reducing LDL-C with statin therapy on cancer incidence or mortality at any of 23 individual categories of sites, with increasing years of treatment, for any individual statin, or in any given subgroup. In particular, among individuals with low baseline LDL-C (<2 mmol/L), there was no evidence that further LDL-C reduction (from about 1.7 to 1.3 mmol/L) increased cancer risk (381 [1.6% py] versus 408 [1.7% py]; RR 0.92 [99% CI 0.76–1.10]).</p> <p>Conclusions: In 27 randomised trials, a median of five years of statin therapy had no effect on the incidence of, or mortality from, any type of cancer (or the aggregate of all cancer).</p&gt

Novel and traditional lipid profiles in Metabolic Syndrome reveal a high atherogenicity

Low-density-lipoprotein cholesterol (LDL-c) guides lipid-lowering therapy, although other lipid parameters could better reflect cardiovascular disease (CVD) risk. Discordance between these parameters and LDL-c has not been evaluated in metabolic syndrome (MetS) patients. We characterized a comprehensive lipid profile in 177 MetS patients. The 2016 ESC/EAS Guidelines for the Management of Dyslipidemias were used to define LDL-c targets. The atherogenic lipoprotein profile was compared in patients with LDL-c within and above the target. Only 34.4% (61) of patients had mean LDL-c levels within the guidelines and patients with LDL-c above target presented significantly elevated levels of Apolipoprotein B (ApoB), non-high-density lipoprotein cholesterol (non-HDL-c) and oxidized LDL-c. In patients with LDL-c within target, 25%, 31% and 49% presented levels above the recommended range for ApoB, non-HDL-c and oxidized LDL-c, respectively. Patients presented a strong association of LDL-c and non-HDL-c (r = 0.796), ApoB (r = 0.749) and oxidized LDL-c (r = 0.452). Similarly, non-HDL-c was strongly correlated with ApoB (r = 0.857) and oxidized-LDL-c (r = 0.555). The logistic regression model evidenced higher triglycerides and HDL-c and lower ApoB as predictors of having LDL-c within target. Reliance solely on LDL-c could result in missed opportunities for CVD risk reduction. ApoB, oxidized LDL-c, and particularly non-HDL-c, could be valuable parameters to estimate the CVD risk of MetS patients and have the potential to be targeted therapeutically

Evaluation of different formulas for LDL-C calculation

<p>Abstract</p> <p>Background</p> <p>Friedewald's formula for the estimation of LDL-C concentration is the most often used formula in clinical practice. A recent formula by Anandaraja and colleagues for LDL-C estimation still needs to be evaluated before it is extensively applied in diagnosis. In the present study we validated existing formulas and derived a more accurate formula to determine LDL-C in a Serbian population.</p> <p>Methods</p> <p>Our study included 2053 patients with TG ≤ 4.52 mmol/L. In an initial group of 1010 patients, Friedewald's and Anandaraja's formulas were compared to a direct homogenous method for LDL-C determination. The obtained results allowed us to modify Friedewald's formula and apply it in a second group of patients.</p> <p>Results</p> <p>The mean LDL-C concentrations were 3.9 ± 1.09 mmol/L, 3.63 ± 1.06 mmol/L and 3.72 ± 1.04 mmol/L measured by a direct homogenous assay (D-LDL-C), calculated by Friedewald's formula (F-LDL-C) and calculated by Anandaraja's formula (A-LDL-C), respectively in the 1010 patients. The Student's paired t-test showed that D-LDL-C values were significantly higher than F-LDL-C and A-LDL-C values (p < 0.001). The Passing-Bablok regression analysis indicated good correlation between calculated and measured LDL-Cs (r > 0.89). Using lipoprotein values from the initial group we modified Friedewald's formula by replacing the term 2.2 with 3. The new modified formula for LDL-C estimation (S-LDL-C) showed no statistically significant difference compared to D-LDL-C. The absolute bias between these two methods was -0.06 ± 0.37 mmol/L with a high correlation coefficient (r = 0.96).</p> <p>Conclusions</p> <p>Our modified formula for LDL-C estimation appears to be more accurate than both Friedewald's and Anandaraja's formulas when applied to a Serbian population.</p

The impact of adjusting for baseline in pharmacogenomic genome-wide association studies of quantitative change.

In pharmacogenomic studies of quantitative change, any association between genetic variants and the pretreatment (baseline) measurement can bias the estimate of effect between those variants and drug response. A putative solution is to adjust for baseline. We conducted a series of genome-wide association studies (GWASs) for low-density lipoprotein cholesterol (LDL-C) response to statin therapy in 34,874 participants of the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort as a case study to investigate the impact of baseline adjustment on results generated from pharmacogenomic studies of quantitative change. Across phenotypes of statin-induced LDL-C change, baseline adjustment identified variants from six loci meeting genome-wide significance (SORT/CELSR2/PSRC1, LPA, SLCO1B1, APOE, APOB, and SMARCA4/LDLR). In contrast, baseline-unadjusted analyses yielded variants from three loci meeting the criteria for genome-wide significance (LPA, APOE, and SLCO1B1). A genome-wide heterogeneity test of baseline versus statin on-treatment LDL-C levels was performed as the definitive test for the true effect of genetic variants on statin-induced LDL-C change. These findings were generally consistent with the models not adjusting for baseline signifying that genome-wide significant hits generated only from baseline-adjusted analyses (SORT/CELSR2/PSRC1, APOB, SMARCA4/LDLR) were likely biased. We then comprehensively reviewed published GWASs of drug-induced quantitative change and discovered that more than half (59%) inappropriately adjusted for baseline. Altogether, we demonstrate that (1) baseline adjustment introduces bias in pharmacogenomic studies of quantitative change and (2) this erroneous methodology is highly prevalent. We conclude that it is critical to avoid this common statistical approach in future pharmacogenomic studies of quantitative change